J. Michael Steele

• V. Pozdnyakov and J. Michael Steele, “Scan Statistics: Pattern Relations and Martingale Methods”. In Handbook of Scan Statistics, edited by (J. Glaz, et al), Springer Verlag, (2019)
• A. Arlotto and J. Michael Steele (2018), A Central Limit Theorem for Costs in Bulinskaya’s Inventory Management Problem When Deliveries Face Delays, Methodology and Computing in Applied Probability: Special Issue in Memory of Moshe Shaked, 41 (4), pp. 1448-1468.
• J. Michael Steele (2016), The Bruss-Robertson Inequality: Elaborations, Extensions, and Applications, Mathematica Applicanda (Annales Societatis Mathematicae Polonae Series III), 44 (1) (), pp. 3-16.
• A. Arlotto and J. Michael Steele (2016), A Central Limit Theorem for Temporally Non-Homogenous Markov Chains with Applications to Dynamic Programming, Mathematics of Operations Research, 41 (4), pp. 1448-1468.
• Peichao Peng and J. Michael Steele (2016), Sequential Selection of a Monotone Subsequence from a Random Permutation, Proceedings of the American Mathematics Society, 144 (11), pp. 4973-4982.
• A. Arlotto, Elchanan Mossel, J. Michael Steele (2016), Quickest Online Selection of an Increasing Subsequence of Specified Size, Random Structures and Algorithms, 49 (), pp. 235-252.
• A. Arlotto and J. Michael Steele (2016), Beardwood-Halton-Hammersly Theorem for Stationary Ergodic Sequences: a Counter-example, Annals of Applied Probability, 26 (4), pp. 2141-2168.
• V. Posdnyakov and J. Michael Steele (2016), Buses, Bullies, and Bijections, Mathematics Magazine, 89 (3), pp. 167-176. Related
  Links

  MAA Allendoefer Award, 2017
• S. Bhamidi, J. Michael Steele, T. Zaman (2015), Twitter Event Networks and the Superstar Model, Annals of Applied Probability, 25 (5), pp. 2462-2502.
• A. Arlotto, V. Nguyen, J. Michael Steele (2015), Optimal Online Selection of a Monotone Subsequence: A Central Limit Theorem, Stochastic Processes and their Applications, 125 (), pp. 3596-3622.
• All Research from J. Michael Steele »

Past Courses

• MATH5460 - Adv Applied Probability

  The required background is (1) enough math background to understand proof techniques in real analysis (closed sets, uniform covergence, fourier series, etc.) and (2) some exposure to probability theory at an intuitive level (a course at the level of Ross's probability text or some exposure to probability in a statistics class).

• STAT4330 - Stochastic Processes

  An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

• STAT5330 - Stochastic Processes

  An introduction to Stochastic Processes. The primary focus is on Markov Chains, Martingales and Gaussian Processes. We will discuss many interesting applications from physics to economics. Topics may include: simulations of path functions, game theory and linear programming, stochastic optimization, Brownian Motion and Black-Scholes.

• STAT9550 - Stoch Cal & Fin Appl

  Selected topics in the theory of probability and stochastic processes.

• STAT9910 - Sem in Adv Appl of Stat

  This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

• STAT9950 - Dissertation

  Dissertation

• Wharton Undergraduate Excellence in Teaching Award, 2010
• Frank Wilcoxon Prize, American Society for Quality Control and the American Statistical Association, 1990
• Fellow, American Statistical Association, 1989
• Fellow, Institute for Mathematical Statistics, 1984

Knowledge @ Wharton

• College Football with Ken Pomeroy, Knowledge @ Wharton - 11/13/2024
• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
• Is Algorithmic Management Too Controlling?, Knowledge @ Wharton - 11/12/2024
• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024